Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution

Geochemical Anomaly Detection in the Irankuh District Using Hybrid Machine Learning Technique and Fractal Modeling

Prediction of elemental concentrations is essential in mineral exploration as it plays a vital role in detailed exploration. New machine learning (ML) methods, such as hybrid models, are robust approaches infrequently used concerning other methods in this field; therefore, they have not been examined properly. In this study, a hybrid machine learning (HML) method was proposed based on combining K-Nearest Neighbor Regression (KNNR) and Random Forest Regression (RFR) to predict Pb and Zn grades in the Irankuh district, Sanandaj-Sirjan Zone.. The aim of the proposed study is to employ the hybrid model as a new method for grade distribution. The KNNR-RFR hybrid model results have been applied for the Pb and Zn anomalies classification. The hybrid (KNNR-RFR) method has shown more accurate prediction outputs based on the correlation coefficients than the single regression models with 0.66 and 0.54 correlation coefficients for Pb and Zn, respectively. The KNN-RF results were used to classify Pb and Zn anomalies in the study area. The concentration-area fractal model separated the main anomalous areas for these elements. The Pb and Zn main anomalies were correlated with mining activities and core drilling data. The current study demonstrates that the hybrid model has a substantial potential for the ore elemental distribution prediction. The presented model expresses a promising result and can predict ore grades in similar investigations

Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran

Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of individual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution

Application of fractal models to outline mineralized zones in the Zaghia iron ore deposit, Central Iran

Recognition of different mineralized zones in an ore deposit is important for mine planning. This study aims to separate the different mineralized zones and the wall rock in the Zaghia iron ore deposit situated in central Iran using the number–size (N–S) and concentration–volume (C–V) fractal methods. The N–S model reveals three geochemical zones defined by Fe thresholds of 24% and 40%, with zones < 24% Fe representing weakly mineralized zones and wall rocks. The C–V model reveals four geochemical zones defined by Fe thresholds of 18%, 30% and 35%, with zones < 18% Fe representing non-mineralized wall rocks. Both the N–S and C–V models indicate that high grade mineralization is situated in the northern part of the ore deposit. The results of validation of the fractal models with the geological model show that the N–S fractal model of highly mineralized zones is better than the C–V fractal model of highly mineralized zones. However, results obtained by means of the C–V fractal model for weakly and moderately mineralized zones are more accurate than the zones obtained by means of the N–S fractal model

Application of number-size (N-S) fractal model to quantify of the vertical distributions of Cu and Mo in Nowchun porphyry deposit (Kerman, SE Iran)/ Zastosowanie modelu fraktalnego n-s (liczba-rozmiar) do ilościowego określenia pionowego rozkładu Cu i Mo w złożu porfirowym (Kerman, Iran)

Determination of the vertical distribution of geochemical elemental concentrations is of fundamental importance in mineral exploration. In this paper, eight mineralized boreholes from the Nowchun Cu-Mo porphyry deposit, SE Iran, were used to identify of the vertical distribution directional properties of Cu and Mo values using number-size (N-S) fractal model. The vertical distributions of Cu and Mo in the mineralized boreholes show a positively skewed distribution in the former and a multimodal distribution in the latter types. Elemental threshold values for the mineralized boreholes were computed by fractal model and compared with the statistical methods based on the data obtained from chemical analysis of samples. Elemental distributions are not normal in these boreholes and their median equal to Cu and Mo thresholds. The results of N-S fractal analysis reveal that Cu and Mo values in mineralized boreholes are multifractals in nature. There are at least three geochemical populations for Cu and Mo in the boreholes and Cu and Mo thresholds have ranges between 0.07%-0.3% and 50-200 ppm, respectively. The results obtained by N-S fractal model were compared with geological observations in the boreholes. Major Cu and Mo enrichment correlated by monzonitic rocks and high amounts of observed Cu and Mo ores (Chalcopyrite and molybdenite) in the boreholes.\ud \ud Określenie pionowego rozkładu stężenia danych pierwiastków chemicznych ma podstawowe znaczenie w trakcie prac poszukiwawczych. W artykule wykorzystano dane z ośmiu otworów w porfirytowym złożu Cu-Mo w Nowchum, w południowo-wschodnim Iranie, dla określenia pionowego rozkładu kierunkowych właściwości i poziomu zawartości Cu i Mo z wykorzystaniem modelu fraktalnego (N-S). Rozkłady pionowe Cu i Mo w otworach wykazują skośną orientację (Cu) i rozkład multimodalny dla Mo. Wartości progowe pierwiastków w otworach obliczono na podstawie modelu fraktalnego i porównano z wynikami uzyskanymi przy użyciu metod statystycznych w oparciu o wyniki analizy chemicznej próbek. Rozkłady wartości pierwiastków w tych otworach nie są rozkładami normalnymi, a ich mediany równe są wartościom progowym dla Cu i Mo. Wyniki analizy fraktalnej wykazują, że wartości Cu i Mo w otworach mają charakter multifraktalny. Mamy do czynienia z co najmniej trzema geochemicznymi populacjami Cu i Mo w otworach a wartości progowe Cu i Mo wahają się w granicach 0.07-0.3% (50-200 ppm). Wyniki uzyskane przy pomocy modelu fraktalnego N-S zostały porównane z wynikami obserwacji geologicznych poczynionych w otworze. Wysokie poziomy wzbogacenia w Cu i Mo skorelowane są z obecnością skał monzonitycznych i wysokimi ilościami rud bogatych w Cu i Mo (chalkopiryt, molibdenit) w otworach